Battery digital twin with physics-based modeling, battery data and machine learning

Li, Weihan; Sauer, Dirk Uwe (Thesis advisor); Viswanathan, Venkat (Thesis advisor)

Aachen : ISEA (2021, 2022)
Book, Dissertation / PhD Thesis

In: Aachener Beiträge des ISEA 164
Page(s)/Article-Nr.: 1 Online-Ressource : Illustrationen, Diagramme

Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021

Abstract

Lithium-ion batteries are developing into a widely used technology due to their low associated costs and high energy density. However, lithium-ion batteries also undergo performance degradation with time during usage as well as storage, which increases the need for evaluation of the longevity and reliability of the cell under operation. Appropriate monitoring of the health of lithium-ion batteries and accurate prediction of the battery degradation not only benefits maintenance, safety and asset optimization but also serves as a starting point for the technical and economic analysis of possible second-life applications. However, accurate estimation, prediction and optimization of the degradation is not a trivial task, as the aging of lithium-ion batteries is a complex nonlinear process with various internal mechanisms whose dynamics are highly challenging to measure and model accurately. In this thesis, the obstacles of the traditional battery management systems in computational power and data storage capability are overcome with the Internet of Things and cloud computing. Battery data are measured and transmitted to the cloud seamlessly to build up the digital twin for the battery system, where intelligent algorithms evaluate the data, extend the battery life and improve the battery reliability. To monitor, predict and optimize the battery use over the whole life cycle with the battery digital twin, comprehensive cloud battery management functionalities are developed in this thesis by integrating physics-based and machine learning models and investigating the role of battery data from laboratory and field operation in safe and reliable battery use.

Institutions

• Institute of Power Electronics and Electrical Drives [614500]
• Chair of Electrochemical Energy Conversion and Storage Systems [618310]